| 2008 |
|---|
| 12 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
From Biomedical Literature to Knowledge: Mining Protein-Protein Interactions.
Computational Intelligence in Biomedicine and Bioinformatics 2008: 397-421 |
| 11 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model.
IJBRA 4(1): 64-80 (2008) |
| 2007 |
|---|
| 10 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
Semi-supervised Learning of the Hidden Vector State Model for Protein-Protein Interactions Extraction.
CIDM 2007: 674-680 |
| 9 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
Training the Hidden Vector State Model from Un-annotated Corpus.
International Conference on Computational Science (2) 2007: 378-385 |
| 8 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh,
Hao Wang:
Ant-MST: An Ant-Based Minimum Spanning Tree for Gene Expression Data Clustering.
PRIB 2007: 198-205 |
| 7 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
Semi-supervised learning of the hidden vector state model for extracting protein-protein interactions.
Artificial Intelligence in Medicine 41(3): 209-222 (2007) |
| 2006 |
|---|
| 6 | EE | Yun Zheng,
Chee Keong Kwoh:
Informative MicroRNA Expression Patterns for Cancer Classification.
BioDM 2006: 143-154 |
| 5 | EE | Seng Hong Seah,
Chee Keong Kwoh,
Vladimir Brusic,
Meena Kishore Sakharkar,
Geok See Ng:
Functional Prediction of Snake Neurotoxins.
ICARCV 2006: 1-4 |
| 4 | EE | Deyu Zhou,
Yulan He,
Chee Keong Kwoh:
Extracting Protein-Protein Interactions from the Literature Using the Hidden Vector State Model.
International Conference on Computational Science (2) 2006: 718-725 |
| 3 | EE | Yun Zheng,
Chee Keong Kwoh:
Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks.
IJDMB 1(2): 111-137 (2006) |
| 2 | EE | Yun Zheng,
Chee Keong Kwoh:
Cancer Classification With MicroRNA Expression Patterns Found By An Information Theory Approach.
JCP 1(5): 30-39 (2006) |
| 2004 |
|---|
| 1 | EE | Zonglin Zhou,
Chee Keong Kwoh:
The Pattern Classification Based on the Nearest Feature Midpoints.
ICPR (3) 2004: 446-449 |
